An Improved Task Allocation Strategy in Cloud using Modified K-means Clustering Technique

In the present era, cloud computing has earned much popularity, mainly because of its utilities and relevance with the current technological trends. It is an arrangement which is highly customizable and encapsulated for providing better computational services to its clients worldwide. In cloud compu...

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Veröffentlicht in:Egyptian informatics journal Jg. 21; H. 4; S. 201 - 208
Hauptverfasser: Sharma, Vrajesh, Bala, Manju
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.12.2020
Elsevier
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ISSN:1110-8665, 2090-4754
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Abstract In the present era, cloud computing has earned much popularity, mainly because of its utilities and relevance with the current technological trends. It is an arrangement which is highly customizable and encapsulated for providing better computational services to its clients worldwide. In cloud computing, scheduling plays a pivotal role in the optimal utilization of resources. Prevalent priority based job scheduling strategies are silent in deciding scheduling scheme for tasks with the same priority and strive hard in appropriately allocating jobs to virtual machines. In the recent years, despite of much research in this field, these scheduling algorithms are unable to provide optimal solution and are lacking in one way or the other in their performance and efficiency. Work pertaining to the use of four criteria/credits for deciding priority, with modified K-means clustering technique is scant. Therefore, to eliminate the drawbacks of the prevalent or existing system and to enhance the performance and efficiency of cloud computing, a new credits based scheduling algorithm has been rendered. The proposed system considers four real time parameters/factors namely Task-Length, Task-Priority, Deadline and Cost, as credits and uses Modified K-means Clustering technique for categorizing the cloudlets and virtual machines (VMs). Results indicate that the suggested scheduling algorithm has excelled existing priority-based scheduling strategy and it has been empirically proven with experimental/simulated results in this paper. CloudSim 3.0.3, a Cloud Simulation Tool has been used to implement and test the proposed algorithm.
AbstractList In the present era, cloud computing has earned much popularity, mainly because of its utilities and relevance with the current technological trends. It is an arrangement which is highly customizable and encapsulated for providing better computational services to its clients worldwide. In cloud computing, scheduling plays a pivotal role in the optimal utilization of resources. Prevalent priority based job scheduling strategies are silent in deciding scheduling scheme for tasks with the same priority and strive hard in appropriately allocating jobs to virtual machines. In the recent years, despite of much research in this field, these scheduling algorithms are unable to provide optimal solution and are lacking in one way or the other in their performance and efficiency. Work pertaining to the use of four criteria/credits for deciding priority, with modified K-means clustering technique is scant. Therefore, to eliminate the drawbacks of the prevalent or existing system and to enhance the performance and efficiency of cloud computing, a new credits based scheduling algorithm has been rendered. The proposed system considers four real time parameters/factors namely Task-Length, Task-Priority, Deadline and Cost, as credits and uses Modified K-means Clustering technique for categorizing the cloudlets and virtual machines (VMs). Results indicate that the suggested scheduling algorithm has excelled existing priority-based scheduling strategy and it has been empirically proven with experimental/simulated results in this paper. CloudSim 3.0.3, a Cloud Simulation Tool has been used to implement and test the proposed algorithm.
Author Sharma, Vrajesh
Bala, Manju
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Cites_doi 10.1109/CSC.2011.6138518
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Issue 4
Keywords Efficient Task Scheduling Strategies
High performance network virtualization
Categorization of Tasks
Reduction in Total Computational Cost in Cloud
Scheduling Algorithm in Cloud
Scheduling Strategies in Cloud Computing
Modified K-Means
Task Allocation Strategies
Rearrangement of Jobs in Cloud
Multiple Credits Based Scheduling
Categorization of Virtual Machines
QoS
Reducing Prediction Error in Cloud Computing
Improving Makespan in Cloud
Priority Based Scheduling
Four Credits based Scheduling
Efficiency Improvement in Cloud
Mapping Cloudlets to VMs
Virtualization
Clustering Method
Language English
License This is an open access article under the CC BY-NC-ND license.
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Snippet In the present era, cloud computing has earned much popularity, mainly because of its utilities and relevance with the current technological trends. It is an...
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SubjectTerms Categorization of Tasks
Categorization of Virtual Machines
Clustering Method
Efficiency Improvement in Cloud
Efficient Task Scheduling Strategies
Four Credits based Scheduling
High performance network virtualization
Improving Makespan in Cloud
Mapping Cloudlets to VMs
Modified K-Means
Multiple Credits Based Scheduling
Priority Based Scheduling
QoS
Rearrangement of Jobs in Cloud
Reducing Prediction Error in Cloud Computing
Reduction in Total Computational Cost in Cloud
Scheduling Algorithm in Cloud
Scheduling Strategies in Cloud Computing
Task Allocation Strategies
Virtualization
Title An Improved Task Allocation Strategy in Cloud using Modified K-means Clustering Technique
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